Natural Language Processing (NLP) is a cutting-edge field that enables machines to understand, interpret, & generate human language. It has applications in various domains, such as language translation, text summarization, sentiment analysis, and the development of conversational agents. Large language models (LLMs) have significantly advanced these applications by leveraging vast data to perform tasks with…
RLHF is the standard approach for aligning LLMs. However, recent advances in offline alignment methods, such as direct preference optimization (DPO) and its variants, challenge the necessity of on-policy sampling in RLHF. Offline methods, which align LLMs using pre-existing datasets without active online interaction, have shown practical efficiency and are simpler and cheaper to implement.…
Large language models (LLMs) have excelled in natural language tasks and instruction following, yet they struggle with non-textual data like images and audio. Incorporating speech comprehension could vastly improve human-computer interaction. Current methods rely on automated speech recognition (ASR) followed by LLM processing, missing non-textual cues. A promising approach integrates textual LLMs with speech encoders…
With AI’s support, the real estate business is seeing a revolutionary shift. With the widespread adoption of AI, real estate agents have access to a suite of AI solutions that can transform their business and provide unparalleled service to clients. Some apps use artificial intelligence to help people choose their ideal homes, forecast real estate…
Named Entity Recognition (NER) is vital in natural language processing, with applications spanning medical coding, financial analysis, and legal document parsing. Custom models are typically created using transformer encoders pre-trained on self-supervised tasks like masked language modeling (MLM). However, recent years have seen the rise of large language models (LLMs) like GPT-3 and GPT-4, which…
In the present world, businesses and individuals rely heavily on artificial intelligence, particularly large language models (LLMs), to assist with various tasks. However, these models have significant limitations. One of the main issues is their inability to remember long-term conversations, which makes it difficult to provide consistent and context-aware responses. Additionally, LLMs cannot perform actions…
The primary goal of AI is to create interactive systems capable of solving diverse problems, including those in medical AI aimed at improving patient outcomes. Large language models (LLMs) have demonstrated significant problem-solving abilities, surpassing human scores on exams like the USMLE. While LLMs can enhance healthcare accessibility, they still face limitations in real-world clinical…
Large language models (LLMs), including GPT-4, LLaMA, and PaLM are pushing the boundaries of artificial intelligence. The inference latency of LLMs plays an important role because of LLMs integration in various applications, ensuring a positive user experience and high service quality. However, the LLM service operates within an AR paradigm, generating one token at a…
Natural language processing (NLP) has advanced significantly thanks to neural networks, with transformer models setting the standard. These models have performed remarkably well across a range of criteria. However, they pose serious problems because of their high memory requirements and high computational expense, particularly for applications that demand long-context work. This persistent problem motivates the…
The evaluation of artificial intelligence models, particularly large language models (LLMs), is a rapidly evolving research field. Researchers are focused on developing more rigorous benchmarks to assess the capabilities of these models across a wide range of complex tasks. This field is essential for advancing AI technology as it provides insights into the strengths &…